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    • 2. 发明授权
    • Neighbor cell location averaging
    • 邻居单元位置平均
    • US08938262B2
    • 2015-01-20
    • US13153113
    • 2011-06-03
    • Glenn Donald MacGouganLukas M. MartiRobert MayorRonald K. HuangJason DereYefim Grosman
    • Glenn Donald MacGouganLukas M. MartiRobert MayorRonald K. HuangJason DereYefim Grosman
    • H04W24/00H04W64/00H04W88/08
    • H04W64/00H04W88/08
    • In some implementations, a location of a mobile device can be determined by calculating an average of the locations of wireless signal transmitters that have transmitted signals received by the mobile device. In some implementations, locations are weighted with coefficients and the average is a weighted average. In some implementations, the locations of the wireless signal transmitters are determined based on identification information encoded in the wireless signals received by the mobile device. The identification information can include an identifier for a wireless signal transmitter. The identification information can include characteristics of the received wireless signal that can be used to identify wireless signal transmitters. In some implementations, identification information from one signal can be combined with identification information from another signal to determine a location of a wireless transmitter.
    • 在一些实施方式中,移动设备的位置可以通过计算具有由移动设备接收的发送信号的无线信号发射机的位置的平均值来确定。 在一些实现中,使用系数对位置进行加权,并且平均值是加权平均值。 在一些实现中,基于由移动设备接收的无线信号中编码的识别信息来确定无线信号发射机的位置。 识别信息可以包括无线信号发射机的标识符。 识别信息可以包括可用于识别无线信号发射机的接收的无线信号的特性。 在一些实现中,来自一个信号的识别信息可以与来自另一信号的识别信息组合以确定无线发射机的位置。
    • 9. 发明授权
    • Location estimation using a probability density function
    • 使用概率密度函数的位置估计
    • US08903414B2
    • 2014-12-02
    • US13153069
    • 2011-06-03
    • Lukas M. MartiGlenn Donald MacGouganRobert MayorRonald K. HuangJason DereYefim Grosman
    • Lukas M. MartiGlenn Donald MacGouganRobert MayorRonald K. HuangJason DereYefim Grosman
    • H04M11/04H04M3/42H04W24/00G01S5/02H04W64/00
    • G01S5/0278H04W64/003
    • Methods, program products, and systems of location estimation using a probability density function are disclosed. In general, in one aspect, a server can estimate an effective location of a wireless access gateway using harvested data. The server can harvest location data from multiple mobile devices. The harvested data can include a location of each mobile device and an identifier of a wireless access gateway that is located within a communication range of the mobile device. The server can calculate an effective location of the wireless access gateway using a probability density function of the harvested data. The probability density function can be a sufficient statistic of the received set of location coordinates for calculating an effective location of the wireless access gateway. The server can send the effective location of the wireless access gateway to other mobile devices for estimating locations of the other mobile devices.
    • 公开了使用概率密度函数的方法,程序产品和位置估计系统。 通常,在一个方面,服务器可以使用收获的数据估计无线接入网关的有效位置。 服务器可以从多个移动设备收集位置数据。 所收获的数据可以包括每个移动设备的位置和位于移动设备的通信范围内的无线接入网关的标识符。 服务器可以使用收获的数据的概率密度函数来计算无线接入网关的有效位置。 概率密度函数可以是用于计算无线接入网关的有效位置的所接收的位置坐标集合的足够的统计量。 服务器可以将无线接入网关的有效位置发送到其他移动设备,以估计其他移动设备的位置。